Affiliations: [a] Suzhou Polytechnic Institute of Agriculture, Suzhou, Jiangsu, China | [b] School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, Jiangsu, China
Correspondence:
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Corresponding author: Haiming Liu, Suzhou Polytechnic Institute of Agriculture, Suzhou, Jiangsu 215008, China. E-mail: 2346460@qq.com.
Abstract: The growth state of flowers is affected by many factors such as temperature, humidity, and light. Therefore, the maintenance of flowers often requires more professional knowledge. Ordinary people are often at a loss when face with various flower representations and do not know where the problem is. In response to the above problems, this article proposes the use of deep learning to identify the growth status of flowers to assist people in successfully raising flowers. In this article, we propose that the mainstream convolutional neural network has the limitation of only inputting images. In terms of network input, data of the current growth environment of flowers will also be input to supplement the input data of the network. In view of the lack of information interaction in the network, in terms of network structure, the shallow and deep characteristics of the network are integrated to make the network performance more advantageous. Experiments show that this method can effectively improve the recognition rate of flower growth status, so as to correctly distinguish the current growth status of flowers.
